CyFormer: Accurate State-of-Health Prediction of Lithium-Ion Batteries via Cyclic Attention

Predicting the State-of-Health (SoH) of lithium-ion batteries is a fundamental task of battery management systems on electric vehicles. It aims at estimating future SoH based on historical aging data. Most existing deep learning methods rely on filter-based feature extractors (e.g., CNN or Kalman filters) and recurrent time sequence models. Though efficient, they generally ignore cyclic features and the domain gap between training and testing batteries. To address this problem, we present CyFormer, a transformer-based cyclic time sequence model for SoH prediction. Instead of the conventional CNN-RNN structure, we adopt an encoder-decoder architecture. In the encoder, row-wise and column-wise attention blocks effectively capture intra-cycle and inter-cycle connections and extract cyclic features. In the decoder, the SoH queries cross-attend to these features to form the final predictions. We further utilize a transfer learning strategy to narrow the domain gap between the training and testing set. To be specific, we use fine-tuning to shift the model to a target working condition. Finally, we made our model more efficient by pruning. The experiment shows that our method attains an MAE of 0.75\% with only 10\% data for fine-tuning on a testing battery, surpassing prior methods by a large margin. Effective and robust, our method provides a potential solution for all cyclic time sequence prediction tasks

Recent progress in anodic oxidation of TiO2 nanotubes and enhanced photocatalytic performance: a short review

© 2021 World Scientific Publishing Company. This is the accepted version of the final published version found at https://doi.org/10.1142/S1793292021300024By adjusting the oxidation voltage, electrolyte, anodizing time and other parameters, TiO2 nanotubes with high aspect ratio can be prepared by oxidation in organic system because anodic oxidation method has the advantage of simple preparation process, low material cost and controllable morphology. Low material cost and controllable morphology by anodizing. This review focuses on the influence of anodizing parameters on the morphology of TiO2 nanotube arrays prepared by anodizing. In order to improve the photocatalytic activity of TiO2 nanotubes under visible light and prolong the life of photo-generated carriers, the research status of improving the photocatalytic activity of TiO2 nanotubes in recent years is reviewed. This review focuses on the preparation and modification of TiO2 nanotubes by anodic oxidation, which is helpful to understand the best structure of TiO2 nanotubes and the appropriate modification methods, thus guiding the application of TiO2 nanotubes in practical photocatalysis. Finally, the development of TiO2 nanotubes is prospected.Peer reviewe

Progress of rapid detection of pesticides in fruits and vegetables

Pesticide residues in fruits and vegetables present a significant concern for human health and safety. By 2022, an average of 3 million people worldwide is poisoned by pesticides every year, and the mortality rate can reach about 20%. This comprehensive review summarizes recent research on the detection of pesticide residues, focusing on the main detection methods and their implications. The study highlights the growing importance of biosensors as a prominent technique, offering enhanced efficiency and accuracy in pesticide residue analysis. The review addresses the challenges associated with pretreatment methods and discusses the advantages and limitations of biosensors. Furthermore, it emphasizes the need for further research to optimize the adaptive capabilities of biosensors, particularly their anti-interference abilities. The findings underscore the significance of developing intelligent adaptive sensors for on-site pesticide residue detection, eliminating the need for complex sample pretreatment. This comprehensive review serves as a valuable reference, facilitating future advancements in pesticide residue analysis, ensuring food safety, and safeguarding consumer health in modern agriculture

PHI-SMFE: spatial multi-scale feature extract neural network based on physical heterogeneous interaction for solving passive scalar advection in a 2-D unsteady flow

Fluid dynamic calculations play a crucial role in understanding marine biochemical dynamic processes, impacting the behavior, interactions, and distribution of biochemical components in aquatic environments. The numerical simulation of fluid dynamics is a challenging task, particularly in real-world scenarios where fluid motion is highly complex. Traditional numerical simulation methods enhance accuracy by increasing the resolution of the computational grid. However, this approach comes with a higher computational demand. Recent advancements have introduced an alternative by leveraging deep learning techniques for fluid dynamic simulations. These methods utilize discretized learned coefficients to achieve high-precision solutions on low-resolution grids, effectively reducing the computational burden while maintaining accuracy. Yet, existing fluid numerical simulation methods based on deep learning are limited by their single-scale analysis of spatially correlated physical fields, which fails to capture the diverse scale characteristics inherent in flow fields governed by complex laws in different physical space. Additionally, these models lack an effective approach to enhance correlation interactions among dynamic fields within the same system. To tackle these challenges, we propose the Spatial Multi-Scale Feature Extract Neural Network based on Physical Heterogeneous Interaction (PHI-SMFE). The PHI module is designed to extract heterogeneity and interaction information from diverse dynamic fields, while the SMFE module focuses on capturing multi-scale features in fluid dynamic fields. We utilize channel-biased convolution to implement a separation strategy, reducing the processing of redundant feature information. Furthermore, the traditional solution module based on the finite volume method is integrated into the network to facilitate the numerical solution of the discretized dynamic field in subsequent time steps. Comparative analysis with the current state-of-the-art model reveals that our proposed method offers a 41% increase in simulation accuracy and a 12.7% decrease in inference time during the iterative evolution of unsteady flow. These results underscore the superior performance of our model in terms of both simulation accuracy and computational speedup, establishing it as a state-of-the-art solution